Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study
Authors' conclusion
We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.
Study context
- Population
- ARIC Study participants aged 47–70 years, 57% women, 19% Black; external validation in Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 cases, 1,214 controls)2,147 incident cases
- Participants
- 8,923 participants
- Outcome
- Associations of 4,955 plasma proteins with incident diabetes
- Follow-up
- Median of 19 years
Methodological detailsdiscovery and internal validation, external validation in nested case-control, Cox regression, elastic net regression, pathway analysis, genetic instrument analysis
- Discovery and internal validation
- External validation in nested case-control
- Cox regression
- Elastic net regression
- Pathway analysis
- Genetic instrument analysis
Main finding
As reported in the abstract
Proteins associated with incident diabetes in discovery (n = 6,010), adjusted for 11 risk factors (P < 10-5)
140
Proteins remaining significant in internal validation (n = 2,913) (P < 0.05/140)
64 of the 140
Of 63 available proteins, validated in MEC
47
75%
Novel associations with diabetes among the 47 proteins
22
Observational data: associations, not effects.
Further findings
Risk prediction model (27 proteins selected by elastic net)
- C statistic in internal validation
- 0.731
- ΔC statistic beyond 13 risk factors incl. fasting glucose and HbA1c (P = 0.04)
- 0.011
Pathway analysis
- Inflammation and lipid metabolism pathways overrepresented among diabetes-associated proteins
Genetic instrument (causality) analyses
- Plasma SHBG, ATP1B2 and GSTA1 suggested to play causal roles in diabetes risk
Original publication
Composed by AI from the published abstract and checked against it. How Briefs are made →